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Milvus Compute Autoscaling

This guide will show you how to use the KubeDB Autoscaler operator to autoscale the compute resources (CPU/memory) of a Milvus database.

Before You Begin

• You should be familiar with the following KubeDB concepts:

  • Milvus
  • MilvusAutoscaler
  • Compute Autoscaling Overview

• Install the KubeDB Autoscaler operator and a metrics server in your cluster — the VPA recommender needs metrics to produce recommendations.

  $ kubectl get deploy metrics-server -n kube-system
  NAME             READY   UP-TO-DATE   AVAILABLE   AGE
  metrics-server   1/1     1            1           5m
  

• Complete the dependency setup from Prepare Dependencies. It installs MinIO, creates the my-release-minio secret, and installs the etcd operator required by Milvus.

Note: The yaml files used in this tutorial are stored in docs/guides/milvus/autoscaler/compute/yamls folder in GitHub repository kubedb/docs.

Compute Autoscaling — Standalone Milvus

Deploy a standalone Milvus and wait until it is Ready. Then create a MilvusAutoscaler targeting the standalone node:

compute-standalone.yaml

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: MilvusAutoscaler
metadata:
  name: milvus-standalone-compute-autoscaler
  namespace: demo
spec:
  databaseRef:
    name: milvus-standalone
  compute:
    node:
      trigger: "On"
      podLifeTimeThreshold: 1m
      minAllowed:
        cpu: 100m
        memory: 256Mi
      maxAllowed:
        cpu: 1000m
        memory: 2Gi
      resourceDiffPercentage: 10
      controlledResources: ["cpu", "memory"]
  opsRequestOptions:
    apply: IfReady
    timeout: 5m

$ kubectl apply -f https://github.com/kubedb/docs/raw/v2026.7.10/docs/guides/milvus/autoscaler/compute/yamls/compute-standalone.yaml
milvusautoscaler.autoscaling.kubedb.com/milvus-standalone-compute-autoscaler created

The autoscaler creates a VerticalPodAutoscaler (VPA) object. Once the VPA recommender produces a recommendation that differs from the current resources by more than resourceDiffPercentage, the autoscaler creates a VerticalScaling MilvusOpsRequest.

$ kubectl get milvusautoscaler -n demo
NAME                                   AGE
milvus-standalone-compute-autoscaler   59s

The autoscaler runs a VPA recommender (fed by the metrics server) and records the recommendation in its status. Once enough samples are collected, the RecommendationProvided condition becomes True and a target resource set is published:

$ kubectl get milvusautoscaler milvus-standalone-compute-autoscaler -n demo -o jsonpath='{.status}' | jq .
{
  "vpas": [
    {
      "conditions": [
        { "type": "RecommendationProvided", "status": "True", "lastTransitionTime": "2026-06-30T18:40:17Z" }
      ],
      "recommendation": {
        "containerRecommendations": [
          {
            "containerName": "milvus",
            "lowerBound": { "cpu": "100m", "memory": "256Mi" },
            "target":     { "cpu": "143m", "memory": "256Mi" },
            "uncappedTarget": { "cpu": "143m", "memory": "262144k" }
          }
        ]
      },
      "ref": { "containerName": "milvus", "vpaObjectName": "milvus-standalone" }
    }
  ]
}

Here the recommended target is cpu: 143m / memory: 256Mi (the standalone idles well below its 500m request). Because the recommendation differs from the current request by more than resourceDiffPercentage (10%) and stays within minAllowed/maxAllowed, the autoscaler creates a VerticalScaling MilvusOpsRequest. This is recorded in the autoscaler status as a CreateOpsRequest condition:

$ kubectl get milvusautoscaler milvus-standalone-compute-autoscaler -n demo \
    -o jsonpath='{.status.conditions[?(@.type=="CreateOpsRequest")].message}'
Successfully created MilvusOpsRequest demo/mvops-milvus-standalone-xqwkhv

$ kubectl get milvusopsrequest -n demo
NAME                             TYPE              STATUS        AGE
mvops-milvus-standalone-xqwkhv   VerticalScaling   Progressing   2s

The Ops-manager then applies the vertical scaling exactly as in the vertical scaling guide, right-sizing the pod to the recommended resources.

Compute Autoscaling — Distributed Milvus

For a distributed Milvus, the autoscaler is keyed by role (proxy, mixcoord, datanode, querynode, streamingnode). The sample below enables compute autoscaling for all five roles:

compute-distributed.yaml

apiVersion: autoscaling.kubedb.com/v1alpha1
kind: MilvusAutoscaler
metadata:
  name: milvus-compute-autoscaler
  namespace: demo
spec:
  databaseRef:
    name: milvus-cluster
  compute:
    proxy:
      trigger: "On"
      podLifeTimeThreshold: 1m
      minAllowed:
        cpu: 100m
        memory: 256Mi
      maxAllowed:
        cpu: 1000m
        memory: 2Gi
      resourceDiffPercentage: 10
      controlledResources: ["cpu", "memory"]
    mixcoord: { ... }
    datanode: { ... }
    querynode: { ... }
    streamingnode: { ... }
  opsRequestOptions:
    apply: IfReady
    timeout: 5m

(Each role block is identical to the proxy block above; see the full file in the yamls folder.)

The behavior is identical to standalone, except a VPA object and resource recommendation are produced per role. When any role’s recommendation differs from its current resources by more than resourceDiffPercentage, the autoscaler creates a VerticalScaling MilvusOpsRequest scoped to that role (see the vertical scaling guide for what that ops request looks like).

Cleaning up

$ kubectl delete milvusautoscaler -n demo --all
$ kubectl delete milvus.kubedb.com -n demo milvus-standalone
$ kubectl delete ns demo

Next Steps

• Learn about storage autoscaling.
• Want to hack on KubeDB? Check our contribution guidelines.